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Data partitioning is the process of dividing a large dataset into smaller, more manageable parts called partitions. Each partition is stored on a separate server or node, allowing for parallel processing and improved performance. Partitioning data plays a crucial role in achieving scalability and handling large volumes of data.

_, on the other hand, involves creating multiple copies of data and storing them on different servers. Replication provides redundancy and improves fault tolerance in case of server failures. It also allows for better distribution of read operations, as multiple servers can handle read requests concurrently.

By combining data partitioning and replication, we can design a system that can handle high volumes of data, provide fault tolerance, and deliver fast and efficient data retrieval.